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For multilayer structures, interfacial failure is one of the most importantelements related to device reliability. For cohesive zone modelling,traction-separation relations represent the adhesive interactions acrossinterfaces. However, existing theoretical models do not currently capturetraction-separation relations that have been extracted using direct methods,particularly under mixed-mode conditions. Given the complexity of the problem,models derived from the neural network approach are attractive. Although theycan be trained to fit data along the loading paths taken in a particular set ofmixed-mode fracture experiments, they may fail to obey physical laws for pathsnot covered by the training data sets. In this paper, a thermodynamicallyconsistent neural network (TCNN) approach is established to model theconstitutive behavior of interfaces when faced with sparse training data sets.Accordingly, three conditions are examined and implemented here: (i)thermodynamic consistency, (ii) maximum energy dissipation path control and(iii) J-integral conservation. These conditions are treated as constraints andare implemented as such in the loss function. The feasibility of this approachis demonstrated by comparing the modeling results with a range of physicalconstraints. Moreover, a Bayesian optimization algorithm is then adopted tooptimize the weight factors associated with each of the constraints in order toovercome convergence issues that can arise when multiple constraints arepresent. The resultant numerical implementation of the ideas presented hereproduced well-behaved, mixed-mode traction separation surfaces that maintainedthe fidelity of the experimental data that was provided as input. The proposedapproach heralds a new autonomous, point-to-point constitutive modeling conceptfor interface mechanics.

Data Driven Modeling of Interfacial Traction Separation Relations using a Thermodynamically Consistent Neural Network